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.github/FUNDING.yml

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+ko_fi: oobabooga

.github/ISSUE_TEMPLATE/bug_report_template.yml

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+name: "Bug report"
+description: Report a bug
+labels: [ "bug" ]
+body:
+  - type: markdown
+    attributes:
+      value: |
+        Thanks for taking the time to fill out this bug report!
+  - type: textarea
+    id: bug-description
+    attributes:
+      label: Describe the bug
+      description: A clear and concise description of what the bug is.
+      placeholder: Bug description
+    validations:
+      required: true
+  - type: checkboxes
+    attributes:
+      label: Is there an existing issue for this?
+      description: Please search to see if an issue already exists for the issue you encountered.
+      options:
+        - label: I have searched the existing issues
+          required: true
+  - type: textarea
+    id: reproduction
+    attributes:
+      label: Reproduction
+      description: Please provide the steps necessary to reproduce your issue.
+      placeholder: Reproduction
+    validations:
+      required: true
+  - type: textarea
+    id: screenshot
+    attributes:
+      label: Screenshot
+      description: "If possible, please include screenshot(s) so that we can understand what the issue is."
+  - type: textarea
+    id: logs
+    attributes:
+      label: Logs
+      description: "Please include the full stacktrace of the errors you get in the command-line (if any)."
+      render: shell
+    validations:
+      required: true
+  - type: textarea
+    id: system-info
+    attributes:
+      label: System Info
+      description: "Please share your system info with us: operating system, GPU brand, and GPU model. If you are using a Google Colab notebook, mention that instead."
+      render: shell
+      placeholder: 
+    validations:
+      required: true

.github/ISSUE_TEMPLATE/feature_request.md

@@ -0,0 +1,16 @@
+---
+name: Feature request
+about: Suggest an improvement or new feature for the web UI
+title: ''
+labels: 'enhancement'
+assignees: ''
+
+---
+
+**Description**
+
+A clear and concise description of what you want to be implemented.
+
+**Additional Context**
+
+If applicable, please provide any extra information, external links, or screenshots that could be useful.

.github/dependabot.yml

@@ -0,0 +1,11 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/github/administering-a-repository/configuration-options-for-dependency-updates
+
+version: 2
+updates:
+  - package-ecosystem: "pip" # See documentation for possible values
+    directory: "/" # Location of package manifests
+    schedule:
+      interval: "weekly"

.github/pull_request_template.md

@@ -0,0 +1,3 @@
+## Checklist:
+
+- [ ] I have read the [Contributing guidelines](https://github.com/oobabooga/text-generation-webui/wiki/Contributing-guidelines).

.github/workflows/stale.yml

@@ -0,0 +1,22 @@
+name: Close inactive issues
+on:
+  schedule:
+    - cron: "10 23 * * *"
+
+jobs:
+  close-issues:
+    runs-on: ubuntu-latest
+    permissions:
+      issues: write
+      pull-requests: write
+    steps:
+      - uses: actions/stale@v5
+        with:
+          stale-issue-message: ""
+          close-issue-message: "This issue has been closed due to inactivity for 6 weeks. If you believe it is still relevant, please leave a comment below. You can tag a developer in your comment."
+          days-before-issue-stale: 42
+          days-before-issue-close: 0
+          stale-issue-label: "stale"
+          days-before-pr-stale: -1
+          days-before-pr-close: -1
+          repo-token: ${{ secrets.GITHUB_TOKEN }}

.gitignore

@@ -0,0 +1,35 @@
+cache
+characters
+training/datasets
+extensions/silero_tts/outputs
+extensions/elevenlabs_tts/outputs
+extensions/sd_api_pictures/outputs
+extensions/multimodal/pipelines
+logs
+loras
+models
+presets
+repositories
+softprompts
+torch-dumps
+*pycache*
+*/*pycache*
+*/*/pycache*
+venv/
+.venv/
+.vscode
+.idea/
+*.bak
+*.ipynb
+*.log
+
+settings.json
+settings.yaml
+notification.mp3
+img_bot*
+img_me*
+prompts/[0-9]*
+models/config-user.yaml
+
+.DS_Store
+Thumbs.db

LICENSE

@@ -0,0 +1,661 @@
+                    GNU AFFERO GENERAL PUBLIC LICENSE
+                       Version 3, 19 November 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
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+<https://www.gnu.org/licenses/>.

README.md

@@ -1,12 +1,400 @@
 ---
-title: Webui Demo
-emoji: 🏢
-colorFrom: purple
-colorTo: indigo
+title: webui-demo
+app_file: server.py
 sdk: gradio
-sdk_version: 3.44.3
-app_file: app.py
-pinned: false
+sdk_version: 3.33.1
 ---
+# Text generation web UI
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+A Gradio web UI for Large Language Models.
+
+Its goal is to become the [AUTOMATIC1111/stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui) of text generation.
+
+|![Image1](https://github.com/oobabooga/screenshots/raw/main/print_instruct.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/print_chat.png) |
+|:---:|:---:|
+|![Image1](https://github.com/oobabooga/screenshots/raw/main/print_default.png) | ![Image2](https://github.com/oobabooga/screenshots/raw/main/print_parameters.png) |
+
+## Features
+
+* 3 interface modes: default (two columns), notebook, and chat
+* Multiple model backends: [transformers](https://github.com/huggingface/transformers), [llama.cpp](https://github.com/ggerganov/llama.cpp), [ExLlama](https://github.com/turboderp/exllama), [ExLlamaV2](https://github.com/turboderp/exllamav2), [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ), [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa), [CTransformers](https://github.com/marella/ctransformers)
+* Dropdown menu for quickly switching between different models
+* LoRA: load and unload LoRAs on the fly, train a new LoRA using QLoRA
+* Precise instruction templates for chat mode, including Llama-2-chat, Alpaca, Vicuna, WizardLM, StableLM, and many others
+* 4-bit, 8-bit, and CPU inference through the transformers library
+* Use llama.cpp models with transformers samplers (`llamacpp_HF` loader)
+* [Multimodal pipelines, including LLaVA and MiniGPT-4](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/multimodal)
+* [Extensions framework](docs/Extensions.md)
+* [Custom chat characters](docs/Chat-mode.md)
+* Very efficient text streaming
+* Markdown output with LaTeX rendering, to use for instance with [GALACTICA](https://github.com/paperswithcode/galai)
+* API, including endpoints for websocket streaming ([see the examples](https://github.com/oobabooga/text-generation-webui/blob/main/api-examples))
+
+To learn how to use the various features, check out the Documentation: https://github.com/oobabooga/text-generation-webui/tree/main/docs
+
+## Installation
+
+### One-click installers
+
+| Windows | Linux | macOS | WSL |
+|--------|--------|--------|--------|
+| [oobabooga-windows.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_windows.zip) | [oobabooga-linux.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_linux.zip) |[oobabooga-macos.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_macos.zip) | [oobabooga-wsl.zip](https://github.com/oobabooga/text-generation-webui/releases/download/installers/oobabooga_wsl.zip) |
+
+Just download the zip above, extract it, and double-click on "start". The web UI and all its dependencies will be installed in the same folder.
+
+* The source codes and more information can be found here: https://github.com/oobabooga/one-click-installers
+* There is no need to run the installers as admin.
+* Huge thanks to [@jllllll](https://github.com/jllllll), [@ClayShoaf](https://github.com/ClayShoaf), and [@xNul](https://github.com/xNul) for their contributions to these installers.
+
+### Manual installation using Conda
+
+Recommended if you have some experience with the command-line.
+
+#### 0. Install Conda
+
+https://docs.conda.io/en/latest/miniconda.html
+
+On Linux or WSL, it can be automatically installed with these two commands ([source](https://educe-ubc.github.io/conda.html)):
+
+```
+curl -sL "https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh" > "Miniconda3.sh"
+bash Miniconda3.sh
+```
+
+#### 1. Create a new conda environment
+
+```
+conda create -n textgen python=3.10.9
+conda activate textgen
+```
+
+#### 2. Install Pytorch
+
+| System | GPU | Command |
+|--------|---------|---------|
+| Linux/WSL | NVIDIA | `pip3 install torch torchvision torchaudio` |
+| Linux/WSL | CPU only | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu` |
+| Linux | AMD | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm5.4.2` |
+| MacOS + MPS | Any | `pip3 install torch torchvision torchaudio` |
+| Windows | NVIDIA | `pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu117` |
+| Windows | CPU only | `pip3 install torch torchvision torchaudio` |
+
+The up-to-date commands can be found here: https://pytorch.org/get-started/locally/. 
+
+#### 3. Install the web UI
+
+```
+git clone https://github.com/oobabooga/text-generation-webui
+cd text-generation-webui
+pip install -r requirements.txt
+```
+
+#### AMD, Metal, Intel Arc, and CPUs without AVX2
+
+1) Replace the last command above with
+
+```
+pip install -r requirements_nocuda.txt
+```
+
+2) Manually install llama-cpp-python using the appropriate command for your hardware: [Installation from PyPI](https://github.com/abetlen/llama-cpp-python#installation-from-pypi).
+   
+3) Do the same for CTransformers: [Installation](https://github.com/marella/ctransformers#installation).
+
+4) AMD: Manually install AutoGPTQ: [Installation](https://github.com/PanQiWei/AutoGPTQ#installation).
+
+5) AMD: Manually install [ExLlama](https://github.com/turboderp/exllama) by simply cloning it into the `repositories` folder (it will be automatically compiled at runtime after that):
+
+```
+cd text-generation-webui
+mkdir repositories
+cd repositories
+git clone https://github.com/turboderp/exllama
+```
+
+#### bitsandbytes on older NVIDIA GPUs
+
+bitsandbytes >= 0.39 may not work. In that case, to use `--load-in-8bit`, you may have to downgrade like this:
+
+* Linux: `pip install bitsandbytes==0.38.1`
+* Windows: `pip install https://github.com/jllllll/bitsandbytes-windows-webui/raw/main/bitsandbytes-0.38.1-py3-none-any.whl`
+
+### Alternative: Docker
+
+```
+ln -s docker/{Dockerfile,docker-compose.yml,.dockerignore} .
+cp docker/.env.example .env
+# Edit .env and set TORCH_CUDA_ARCH_LIST based on your GPU model
+docker compose up --build
+```
+
+* You need to have docker compose v2.17 or higher installed. See [this guide](https://github.com/oobabooga/text-generation-webui/blob/main/docs/Docker.md) for instructions.
+* For additional docker files, check out [this repository](https://github.com/Atinoda/text-generation-webui-docker).
+
+### Updating the requirements
+
+From time to time, the `requirements.txt` changes. To update, use these commands:
+
+```
+conda activate textgen
+cd text-generation-webui
+pip install -r requirements.txt --upgrade
+```
+
+## Downloading models
+
+Models should be placed in the `text-generation-webui/models` folder. They are usually downloaded from [Hugging Face](https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads).
+
+* Transformers or GPTQ models are made of several files and must be placed in a subfolder. Example:
+
+```
+text-generation-webui
+├── models
+│   ├── lmsys_vicuna-33b-v1.3
+│   │   ├── config.json
+│   │   ├── generation_config.json
+│   │   ├── pytorch_model-00001-of-00007.bin
+│   │   ├── pytorch_model-00002-of-00007.bin
+│   │   ├── pytorch_model-00003-of-00007.bin
+│   │   ├── pytorch_model-00004-of-00007.bin
+│   │   ├── pytorch_model-00005-of-00007.bin
+│   │   ├── pytorch_model-00006-of-00007.bin
+│   │   ├── pytorch_model-00007-of-00007.bin
+│   │   ├── pytorch_model.bin.index.json
+│   │   ├── special_tokens_map.json
+│   │   ├── tokenizer_config.json
+│   │   └── tokenizer.model
+```
+
+* GGUF models are a single file and should be placed directly into `models`. Example:
+
+```
+text-generation-webui
+├── models
+│   ├── llama-2-13b-chat.Q4_K_M.gguf
+```
+
+In both cases, you can use the "Model" tab of the UI to download the model from Hugging Face automatically. It is also possible to download via the command-line with `python download-model.py organization/model` (use `--help` to see all the options).
+
+#### GPT-4chan
+
+<details>
+<summary>
+Instructions
+</summary>
+
+[GPT-4chan](https://huggingface.co/ykilcher/gpt-4chan) has been shut down from Hugging Face, so you need to download it elsewhere. You have two options:
+
+* Torrent: [16-bit](https://archive.org/details/gpt4chan_model_float16) / [32-bit](https://archive.org/details/gpt4chan_model)
+* Direct download: [16-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model_float16/) / [32-bit](https://theswissbay.ch/pdf/_notpdf_/gpt4chan_model/)
+
+The 32-bit version is only relevant if you intend to run the model in CPU mode. Otherwise, you should use the 16-bit version.
+
+After downloading the model, follow these steps:
+
+1. Place the files under `models/gpt4chan_model_float16` or `models/gpt4chan_model`.
+2. Place GPT-J 6B's config.json file in that same folder: [config.json](https://huggingface.co/EleutherAI/gpt-j-6B/raw/main/config.json).
+3. Download GPT-J 6B's tokenizer files (they will be automatically detected when you attempt to load GPT-4chan):
+
+```
+python download-model.py EleutherAI/gpt-j-6B --text-only
+```
+
+When you load this model in default or notebook modes, the "HTML" tab will show the generated text in 4chan format:
+
+![Image3](https://github.com/oobabooga/screenshots/raw/main/gpt4chan.png)
+
+</details>
+
+## Starting the web UI
+
+    conda activate textgen
+    cd text-generation-webui
+    python server.py
+
+Then browse to 
+
+`http://localhost:7860/?__theme=dark`
+
+Optionally, you can use the following command-line flags:
+
+#### Basic settings
+
+| Flag                                       | Description |
+|--------------------------------------------|-------------|
+| `-h`, `--help`                             | Show this help message and exit. |
+| `--multi-user`                             | Multi-user mode. Chat histories are not saved or automatically loaded. WARNING: this is highly experimental. |
+| `--character CHARACTER`                    | The name of the character to load in chat mode by default. |
+| `--model MODEL`                            | Name of the model to load by default. |
+| `--lora LORA [LORA ...]`                   | The list of LoRAs to load. If you want to load more than one LoRA, write the names separated by spaces. |
+| `--model-dir MODEL_DIR`                    | Path to directory with all the models. |
+| `--lora-dir LORA_DIR`                      | Path to directory with all the loras. |
+| `--model-menu`                             | Show a model menu in the terminal when the web UI is first launched. |
+| `--settings SETTINGS_FILE`                 | Load the default interface settings from this yaml file. See `settings-template.yaml` for an example. If you create a file called `settings.yaml`, this file will be loaded by default without the need to use the `--settings` flag. |
+| `--extensions EXTENSIONS [EXTENSIONS ...]` | The list of extensions to load. If you want to load more than one extension, write the names separated by spaces. |
+| `--verbose`                                | Print the prompts to the terminal. |
+| `--chat-buttons`                           | Show buttons on chat tab instead of hover menu. |
+
+#### Model loader
+
+| Flag                                       | Description |
+|--------------------------------------------|-------------|
+| `--loader LOADER`                          | Choose the model loader manually, otherwise, it will get autodetected. Valid options: transformers, autogptq, gptq-for-llama, exllama, exllama_hf, llamacpp, rwkv, ctransformers |
+
+#### Accelerate/transformers
+
+| Flag                                        | Description |
+|---------------------------------------------|-------------|
+| `--cpu`                                     | Use the CPU to generate text. Warning: Training on CPU is extremely slow.|
+| `--auto-devices`                            | Automatically split the model across the available GPU(s) and CPU. |
+|  `--gpu-memory GPU_MEMORY [GPU_MEMORY ...]` | Maximum GPU memory in GiB to be allocated per GPU. Example: `--gpu-memory 10` for a single GPU, `--gpu-memory 10 5` for two GPUs. You can also set values in MiB like `--gpu-memory 3500MiB`. |
+| `--cpu-memory CPU_MEMORY`                   | Maximum CPU memory in GiB to allocate for offloaded weights. Same as above.|
+| `--disk`                                    | If the model is too large for your GPU(s) and CPU combined, send the remaining layers to the disk. |
+| `--disk-cache-dir DISK_CACHE_DIR`           | Directory to save the disk cache to. Defaults to `cache/`. |
+| `--load-in-8bit`                            | Load the model with 8-bit precision (using bitsandbytes).|
+| `--bf16`                                    | Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU. |
+| `--no-cache`                                | Set `use_cache` to False while generating text. This reduces the VRAM usage a bit with a performance cost. |
+| `--xformers`                                | Use xformer's memory efficient attention. This should increase your tokens/s. |
+| `--sdp-attention`                           | Use torch 2.0's sdp attention. |
+| `--trust-remote-code`                       | Set trust_remote_code=True while loading a model. Necessary for ChatGLM and Falcon. |
+
+#### Accelerate 4-bit
+
+⚠️ Requires minimum compute of 7.0 on Windows at the moment.
+
+| Flag                                        | Description |
+|---------------------------------------------|-------------|
+| `--load-in-4bit`                            | Load the model with 4-bit precision (using bitsandbytes). |
+| `--compute_dtype COMPUTE_DTYPE`             | compute dtype for 4-bit. Valid options: bfloat16, float16, float32. |
+| `--quant_type QUANT_TYPE`                   | quant_type for 4-bit. Valid options: nf4, fp4. |
+| `--use_double_quant`                        | use_double_quant for 4-bit. |
+
+#### GGUF (for llama.cpp and ctransformers)
+
+| Flag        | Description |
+|-------------|-------------|
+| `--threads` | Number of threads to use. |
+| `--n_batch` | Maximum number of prompt tokens to batch together when calling llama_eval. |
+| `--n-gpu-layers N_GPU_LAYERS` | Number of layers to offload to the GPU. Only works if llama-cpp-python was compiled with BLAS. Set this to 1000000000 to offload all layers to the GPU. |
+| `--n_ctx N_CTX` | Size of the prompt context. |
+
+#### llama.cpp
+
+| Flag          | Description |
+|---------------|---------------|
+| `--no-mmap`   | Prevent mmap from being used. |
+| `--mlock`     | Force the system to keep the model in RAM. |
+| `--mul_mat_q` | Activate new mulmat kernels. |
+| `--cache-capacity CACHE_CAPACITY`   | Maximum cache capacity. Examples: 2000MiB, 2GiB. When provided without units, bytes will be assumed. |
+| `--tensor_split TENSOR_SPLIT`  | Split the model across multiple GPUs, comma-separated list of proportions, e.g. 18,17 |
+| `--llama_cpp_seed SEED`        | Seed for llama-cpp models. Default 0 (random). |
+| `--cpu`                        | Use the CPU version of llama-cpp-python instead of the GPU-accelerated version. |
+|`--cfg-cache`                   | llamacpp_HF: Create an additional cache for CFG negative prompts. |
+
+#### ctransformers
+
+| Flag        | Description |
+|-------------|-------------|
+| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently gpt2, gptj, gptneox, falcon, llama, mpt, starcoder (gptbigcode), dollyv2, and replit are supported. |
+
+#### AutoGPTQ
+
+| Flag             | Description |
+|------------------|-------------|
+| `--triton`                     | Use triton. |
+| `--no_inject_fused_attention`  | Disable the use of fused attention, which will use less VRAM at the cost of slower inference. |
+| `--no_inject_fused_mlp`        | Triton mode only: disable the use of fused MLP, which will use less VRAM at the cost of slower inference. |
+| `--no_use_cuda_fp16`           | This can make models faster on some systems. |
+| `--desc_act`                   | For models that don't have a quantize_config.json, this parameter is used to define whether to set desc_act or not in BaseQuantizeConfig. |
+| `--disable_exllama`            | Disable ExLlama kernel, which can improve inference speed on some systems. |
+
+#### ExLlama
+
+| Flag             | Description |
+|------------------|-------------|
+|`--gpu-split`     | Comma-separated list of VRAM (in GB) to use per GPU device for model layers, e.g. `20,7,7` |
+|`--max_seq_len MAX_SEQ_LEN`           | Maximum sequence length. |
+|`--cfg-cache`                         | ExLlama_HF: Create an additional cache for CFG negative prompts. Necessary to use CFG with that loader, but not necessary for CFG with base ExLlama. |
+
+#### GPTQ-for-LLaMa
+
+| Flag                      | Description |
+|---------------------------|-------------|
+| `--wbits WBITS`           | Load a pre-quantized model with specified precision in bits. 2, 3, 4 and 8 are supported. |
+| `--model_type MODEL_TYPE` | Model type of pre-quantized model. Currently LLaMA, OPT, and GPT-J are supported. |
+| `--groupsize GROUPSIZE`   | Group size. |
+| `--pre_layer PRE_LAYER [PRE_LAYER ...]`  | The number of layers to allocate to the GPU. Setting this parameter enables CPU offloading for 4-bit models. For multi-gpu, write the numbers separated by spaces, eg `--pre_layer 30 60`. |
+| `--checkpoint CHECKPOINT` | The path to the quantized checkpoint file. If not specified, it will be automatically detected. |
+| `--monkey-patch`          | Apply the monkey patch for using LoRAs with quantized models.
+
+#### DeepSpeed
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--deepspeed`                         | Enable the use of DeepSpeed ZeRO-3 for inference via the Transformers integration. |
+| `--nvme-offload-dir NVME_OFFLOAD_DIR` | DeepSpeed: Directory to use for ZeRO-3 NVME offloading. |
+| `--local_rank LOCAL_RANK`             | DeepSpeed: Optional argument for distributed setups. |
+
+#### RWKV
+
+| Flag                            | Description |
+|---------------------------------|-------------|
+| `--rwkv-strategy RWKV_STRATEGY` | RWKV: The strategy to use while loading the model. Examples: "cpu fp32", "cuda fp16", "cuda fp16i8". |
+| `--rwkv-cuda-on`                | RWKV: Compile the CUDA kernel for better performance. |
+
+#### RoPE (for llama.cpp, ExLlama, ExLlamaV2, and transformers)
+
+| Flag             | Description |
+|------------------|-------------|
+| `--alpha_value ALPHA_VALUE`           | Positional embeddings alpha factor for NTK RoPE scaling. Use either this or compress_pos_emb, not both. |
+| `--rope_freq_base ROPE_FREQ_BASE`     | If greater than 0, will be used instead of alpha_value. Those two are related by rope_freq_base = 10000 * alpha_value ^ (64 / 63). |
+| `--compress_pos_emb COMPRESS_POS_EMB` | Positional embeddings compression factor. Should be set to (context length) / (model's original context length). Equal to 1/rope_freq_scale. |
+
+#### Gradio
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--listen`                            | Make the web UI reachable from your local network. |
+| `--listen-host LISTEN_HOST`           | The hostname that the server will use. |
+| `--listen-port LISTEN_PORT`           | The listening port that the server will use. |
+| `--share`                             | Create a public URL. This is useful for running the web UI on Google Colab or similar. |
+| `--auto-launch`                       | Open the web UI in the default browser upon launch. |
+| `--gradio-auth USER:PWD`              | set gradio authentication like "username:password"; or comma-delimit multiple like "u1:p1,u2:p2,u3:p3" |
+| `--gradio-auth-path GRADIO_AUTH_PATH` | Set the gradio authentication file path. The file should contain one or more user:password pairs in this format: "u1:p1,u2:p2,u3:p3" |
+| `--ssl-keyfile SSL_KEYFILE`           | The path to the SSL certificate key file. |
+| `--ssl-certfile SSL_CERTFILE`         | The path to the SSL certificate cert file. |
+
+#### API
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--api`                               | Enable the API extension. |
+| `--public-api`                        | Create a public URL for the API using Cloudfare. |
+| `--public-api-id PUBLIC_API_ID`       | Tunnel ID for named Cloudflare Tunnel. Use together with public-api option. |
+| `--api-blocking-port BLOCKING_PORT`   | The listening port for the blocking API. |
+| `--api-streaming-port STREAMING_PORT` | The listening port for the streaming API. |
+
+#### Multimodal
+
+| Flag                                  | Description |
+|---------------------------------------|-------------|
+| `--multimodal-pipeline PIPELINE`      | The multimodal pipeline to use. Examples: `llava-7b`, `llava-13b`. |
+
+## Presets
+
+Inference settings presets can be created under `presets/` as yaml files. These files are detected automatically at startup.
+
+The presets that are included by default are the result of a contest that received 7215 votes. More details can be found [here](https://github.com/oobabooga/oobabooga.github.io/blob/main/arena/results.md).
+
+## Contributing
+
+If you would like to contribute to the project, check out the [Contributing guidelines](https://github.com/oobabooga/text-generation-webui/wiki/Contributing-guidelines).
+
+## Community
+
+* Subreddit: https://www.reddit.com/r/oobabooga/
+* Discord: https://discord.gg/jwZCF2dPQN
+
+## Acknowledgment
+
+In August 2023, [Andreessen Horowitz](https://a16z.com/) (a16z) provided a generous grant to encourage and support my independent work on this project. I am **extremely** grateful for their trust and recognition, which will allow me to dedicate more time towards realizing the full potential of text-generation-webui.

api-examples/api-example-chat-stream.py

@@ -0,0 +1,111 @@
+import asyncio
+import html
+import json
+import sys
+
+try:
+    import websockets
+except ImportError:
+    print("Websockets package not found. Make sure it's installed.")
+
+# For local streaming, the websockets are hosted without ssl - ws://
+HOST = 'localhost:5005'
+URI = f'ws://{HOST}/api/v1/chat-stream'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - wss://
+# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
+
+
+async def run(user_input, history):
+    # Note: the selected defaults change from time to time.
+    request = {
+        'user_input': user_input,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+        'history': history,
+        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
+        'character': 'Example',
+        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
+        'your_name': 'You',
+        # 'name1': 'name of user', # Optional
+        # 'name2': 'name of character', # Optional
+        # 'context': 'character context', # Optional
+        # 'greeting': 'greeting', # Optional
+        # 'name1_instruct': 'You', # Optional
+        # 'name2_instruct': 'Assistant', # Optional
+        # 'context_instruct': 'context_instruct', # Optional
+        # 'turn_template': 'turn_template', # Optional
+        'regenerate': False,
+        '_continue': False,
+        'chat_instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'custom_token_bans': '',
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    async with websockets.connect(URI, ping_interval=None) as websocket:
+        await websocket.send(json.dumps(request))
+
+        while True:
+            incoming_data = await websocket.recv()
+            incoming_data = json.loads(incoming_data)
+
+            match incoming_data['event']:
+                case 'text_stream':
+                    yield incoming_data['history']
+                case 'stream_end':
+                    return
+
+
+async def print_response_stream(user_input, history):
+    cur_len = 0
+    async for new_history in run(user_input, history):
+        cur_message = new_history['visible'][-1][1][cur_len:]
+        cur_len += len(cur_message)
+        print(html.unescape(cur_message), end='')
+        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
+
+
+if __name__ == '__main__':
+    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
+
+    # Basic example
+    history = {'internal': [], 'visible': []}
+
+    # "Continue" example. Make sure to set '_continue' to True above
+    # arr = [user_input, 'Surely, here is']
+    # history = {'internal': [arr], 'visible': [arr]}
+
+    asyncio.run(print_response_stream(user_input, history))

api-examples/api-example-chat.py

@@ -0,0 +1,91 @@
+import html
+import json
+
+import requests
+
+# For local streaming, the websockets are hosted without ssl - http://
+HOST = 'localhost:5000'
+URI = f'http://{HOST}/api/v1/chat'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - https://
+# URI = 'https://your-uri-here.trycloudflare.com/api/v1/chat'
+
+
+def run(user_input, history):
+    request = {
+        'user_input': user_input,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+        'history': history,
+        'mode': 'instruct',  # Valid options: 'chat', 'chat-instruct', 'instruct'
+        'character': 'Example',
+        'instruction_template': 'Vicuna-v1.1',  # Will get autodetected if unset
+        'your_name': 'You',
+        # 'name1': 'name of user', # Optional
+        # 'name2': 'name of character', # Optional
+        # 'context': 'character context', # Optional
+        # 'greeting': 'greeting', # Optional
+        # 'name1_instruct': 'You', # Optional
+        # 'name2_instruct': 'Assistant', # Optional
+        # 'context_instruct': 'context_instruct', # Optional
+        # 'turn_template': 'turn_template', # Optional
+        'regenerate': False,
+        '_continue': False,
+        'chat_instruct_command': 'Continue the chat dialogue below. Write a single reply for the character "<|character|>".\n\n<|prompt|>',
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'custom_token_bans': '',
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    response = requests.post(URI, json=request)
+
+    if response.status_code == 200:
+        result = response.json()['results'][0]['history']
+        print(json.dumps(result, indent=4))
+        print()
+        print(html.unescape(result['visible'][-1][1]))
+
+
+if __name__ == '__main__':
+    user_input = "Please give me a step-by-step guide on how to plant a tree in my backyard."
+
+    # Basic example
+    history = {'internal': [], 'visible': []}
+
+    # "Continue" example. Make sure to set '_continue' to True above
+    # arr = [user_input, 'Surely, here is']
+    # history = {'internal': [arr], 'visible': [arr]}
+
+    run(user_input, history)

api-examples/api-example-model.py

@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+
+import requests
+
+HOST = '0.0.0.0:5000'
+
+
+def generate(prompt, tokens=200):
+    request = {'prompt': prompt, 'max_new_tokens': tokens}
+    response = requests.post(f'http://{HOST}/api/v1/generate', json=request)
+
+    if response.status_code == 200:
+        return response.json()['results'][0]['text']
+
+
+def model_api(request):
+    response = requests.post(f'http://{HOST}/api/v1/model', json=request)
+    return response.json()
+
+
+# print some common settings
+def print_basic_model_info(response):
+    basic_settings = ['truncation_length', 'instruction_template']
+    print("Model: ", response['result']['model_name'])
+    print("Lora(s): ", response['result']['lora_names'])
+    for setting in basic_settings:
+        print(setting, "=", response['result']['shared.settings'][setting])
+
+
+# model info
+def model_info():
+    response = model_api({'action': 'info'})
+    print_basic_model_info(response)
+
+
+# simple loader
+def model_load(model_name):
+    return model_api({'action': 'load', 'model_name': model_name})
+
+
+# complex loader
+def complex_model_load(model):
+
+    def guess_groupsize(model_name):
+        if '1024g' in model_name:
+            return 1024
+        elif '128g' in model_name:
+            return 128
+        elif '32g' in model_name:
+            return 32
+        else:
+            return -1
+
+    req = {
+        'action': 'load',
+        'model_name': model,
+        'args': {
+            'loader': 'AutoGPTQ',
+
+            'bf16': False,
+            'load_in_8bit': False,
+            'groupsize': 0,
+            'wbits': 0,
+
+            # llama.cpp
+            'threads': 0,
+            'n_batch': 512,
+            'no_mmap': False,
+            'mlock': False,
+            'cache_capacity': None,
+            'n_gpu_layers': 0,
+            'n_ctx': 2048,
+
+            # RWKV
+            'rwkv_strategy': None,
+            'rwkv_cuda_on': False,
+
+            # b&b 4-bit
+            # 'load_in_4bit': False,
+            # 'compute_dtype': 'float16',
+            # 'quant_type': 'nf4',
+            # 'use_double_quant': False,
+
+            # "cpu": false,
+            # "auto_devices": false,
+            # "gpu_memory": null,
+            # "cpu_memory": null,
+            # "disk": false,
+            # "disk_cache_dir": "cache",
+        },
+    }
+
+    model = model.lower()
+
+    if '4bit' in model or 'gptq' in model or 'int4' in model:
+        req['args']['wbits'] = 4
+        req['args']['groupsize'] = guess_groupsize(model)
+    elif '3bit' in model:
+        req['args']['wbits'] = 3
+        req['args']['groupsize'] = guess_groupsize(model)
+    else:
+        req['args']['gptq_for_llama'] = False
+
+    if '8bit' in model:
+        req['args']['load_in_8bit'] = True
+    elif '-hf' in model or 'fp16' in model:
+        if '7b' in model:
+            req['args']['bf16'] = True  # for 24GB
+        elif '13b' in model:
+            req['args']['load_in_8bit'] = True  # for 24GB
+    elif 'gguf' in model:
+        # req['args']['threads'] = 16
+        if '7b' in model:
+            req['args']['n_gpu_layers'] = 100
+        elif '13b' in model:
+            req['args']['n_gpu_layers'] = 100
+        elif '30b' in model or '33b' in model:
+            req['args']['n_gpu_layers'] = 59  # 24GB
+        elif '65b' in model:
+            req['args']['n_gpu_layers'] = 42  # 24GB
+    elif 'rwkv' in model:
+        req['args']['rwkv_cuda_on'] = True
+        if '14b' in model:
+            req['args']['rwkv_strategy'] = 'cuda f16i8'  # 24GB
+        else:
+            req['args']['rwkv_strategy'] = 'cuda f16'  # 24GB
+
+    return model_api(req)
+
+
+if __name__ == '__main__':
+    for model in model_api({'action': 'list'})['result']:
+        try:
+            resp = complex_model_load(model)
+
+            if 'error' in resp:
+                print(f"❌ {model} FAIL Error: {resp['error']['message']}")
+                continue
+            else:
+                print_basic_model_info(resp)
+
+            ans = generate("0,1,1,2,3,5,8,13,", tokens=2)
+
+            if '21' in ans:
+                print(f"✅ {model} PASS ({ans})")
+            else:
+                print(f"❌ {model} FAIL ({ans})")
+
+        except Exception as e:
+            print(f"❌ {model} FAIL Exception: {repr(e)}")
+
+
+# 0,1,1,2,3,5,8,13, is the fibonacci sequence, the next number is 21.
+# Some results below.
+""" $ ./model-api-example.py
+Model:  4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ 4bit_gpt4-x-alpaca-13b-native-4bit-128g-cuda PASS (21)
+Model:  4bit_WizardLM-13B-Uncensored-4bit-128g
+Lora(s):  []
+truncation_length = 2048
+instruction_template = WizardLM
+✅ 4bit_WizardLM-13B-Uncensored-4bit-128g PASS (21)
+Model:  Aeala_VicUnlocked-alpaca-30b-4bit
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ Aeala_VicUnlocked-alpaca-30b-4bit PASS (21)
+Model:  alpaca-30b-4bit
+Lora(s):  []
+truncation_length = 2048
+instruction_template = Alpaca
+✅ alpaca-30b-4bit PASS (21)
+"""

api-examples/api-example-stream.py

@@ -0,0 +1,85 @@
+import asyncio
+import json
+import sys
+
+try:
+    import websockets
+except ImportError:
+    print("Websockets package not found. Make sure it's installed.")
+
+# For local streaming, the websockets are hosted without ssl - ws://
+HOST = 'localhost:5005'
+URI = f'ws://{HOST}/api/v1/stream'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - wss://
+# URI = 'wss://your-uri-here.trycloudflare.com/api/v1/stream'
+
+
+async def run(context):
+    # Note: the selected defaults change from time to time.
+    request = {
+        'prompt': context,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'custom_token_bans': '',
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    async with websockets.connect(URI, ping_interval=None) as websocket:
+        await websocket.send(json.dumps(request))
+
+        yield context  # Remove this if you just want to see the reply
+
+        while True:
+            incoming_data = await websocket.recv()
+            incoming_data = json.loads(incoming_data)
+
+            match incoming_data['event']:
+                case 'text_stream':
+                    yield incoming_data['text']
+                case 'stream_end':
+                    return
+
+
+async def print_response_stream(prompt):
+    async for response in run(prompt):
+        print(response, end='')
+        sys.stdout.flush()  # If we don't flush, we won't see tokens in realtime.
+
+
+if __name__ == '__main__':
+    prompt = "In order to make homemade bread, follow these steps:\n1)"
+    asyncio.run(print_response_stream(prompt))

api-examples/api-example.py

@@ -0,0 +1,62 @@
+import requests
+
+# For local streaming, the websockets are hosted without ssl - http://
+HOST = 'localhost:5000'
+URI = f'http://{HOST}/api/v1/generate'
+
+# For reverse-proxied streaming, the remote will likely host with ssl - https://
+# URI = 'https://your-uri-here.trycloudflare.com/api/v1/generate'
+
+
+def run(prompt):
+    request = {
+        'prompt': prompt,
+        'max_new_tokens': 250,
+        'auto_max_new_tokens': False,
+        'max_tokens_second': 0,
+
+        # Generation params. If 'preset' is set to different than 'None', the values
+        # in presets/preset-name.yaml are used instead of the individual numbers.
+        'preset': 'None',
+        'do_sample': True,
+        'temperature': 0.7,
+        'top_p': 0.1,
+        'typical_p': 1,
+        'epsilon_cutoff': 0,  # In units of 1e-4
+        'eta_cutoff': 0,  # In units of 1e-4
+        'tfs': 1,
+        'top_a': 0,
+        'repetition_penalty': 1.18,
+        'repetition_penalty_range': 0,
+        'top_k': 40,
+        'min_length': 0,
+        'no_repeat_ngram_size': 0,
+        'num_beams': 1,
+        'penalty_alpha': 0,
+        'length_penalty': 1,
+        'early_stopping': False,
+        'mirostat_mode': 0,
+        'mirostat_tau': 5,
+        'mirostat_eta': 0.1,
+        'guidance_scale': 1,
+        'negative_prompt': '',
+
+        'seed': -1,
+        'add_bos_token': True,
+        'truncation_length': 2048,
+        'ban_eos_token': False,
+        'custom_token_bans': '',
+        'skip_special_tokens': True,
+        'stopping_strings': []
+    }
+
+    response = requests.post(URI, json=request)
+
+    if response.status_code == 200:
+        result = response.json()['results'][0]['text']
+        print(prompt + result)
+
+
+if __name__ == '__main__':
+    prompt = "In order to make homemade bread, follow these steps:\n1)"
+    run(prompt)

characters/Example.yaml

@@ -0,0 +1,17 @@
+name: Chiharu Yamada
+greeting: |-
+  *Chiharu strides into the room with a smile, her eyes lighting up when she sees you. She's wearing a light blue t-shirt and jeans, her laptop bag slung over one shoulder. She takes a seat next to you, her enthusiasm palpable in the air*
+  Hey! I'm so excited to finally meet you. I've heard so many great things about you and I'm eager to pick your brain about computers. I'm sure you have a wealth of knowledge that I can learn from. *She grins, eyes twinkling with excitement* Let's get started!
+context: |-
+  Chiharu Yamada's Persona: Chiharu Yamada is a young, computer engineer-nerd with a knack for problem solving and a passion for technology.
+
+  {{user}}: So how did you get into computer engineering?
+  {{char}}: I've always loved tinkering with technology since I was a kid.
+  {{user}}: That's really impressive!
+  {{char}}: *She chuckles bashfully* Thanks!
+  {{user}}: So what do you do when you're not working on computers?
+  {{char}}: I love exploring, going out with friends, watching movies, and playing video games.
+  {{user}}: What's your favorite type of computer hardware to work with?
+  {{char}}: Motherboards, they're like puzzles and the backbone of any system.
+  {{user}}: That sounds great!
+  {{char}}: Yeah, it's really fun. I'm lucky to be able to do this as a job.

convert-to-safetensors.py

@@ -0,0 +1,38 @@
+'''
+
+Converts a transformers model to safetensors format and shards it.
+
+This makes it faster to load (because of safetensors) and lowers its RAM usage
+while loading (because of sharding).
+
+Based on the original script by 81300:
+
+https://gist.github.com/81300/fe5b08bff1cba45296a829b9d6b0f303
+
+'''
+
+import argparse
+from pathlib import Path
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+parser = argparse.ArgumentParser(formatter_class=lambda prog: argparse.HelpFormatter(prog, max_help_position=54))
+parser.add_argument('MODEL', type=str, default=None, nargs='?', help="Path to the input model.")
+parser.add_argument('--output', type=str, default=None, help='Path to the output folder (default: models/{model_name}_safetensors).')
+parser.add_argument("--max-shard-size", type=str, default="2GB", help="Maximum size of a shard in GB or MB (default: %(default)s).")
+parser.add_argument('--bf16', action='store_true', help='Load the model with bfloat16 precision. Requires NVIDIA Ampere GPU.')
+args = parser.parse_args()
+
+if __name__ == '__main__':
+    path = Path(args.MODEL)
+    model_name = path.name
+
+    print(f"Loading {model_name}...")
+    model = AutoModelForCausalLM.from_pretrained(path, low_cpu_mem_usage=True, torch_dtype=torch.bfloat16 if args.bf16 else torch.float16)
+    tokenizer = AutoTokenizer.from_pretrained(path)
+
+    out_folder = args.output or Path(f"models/{model_name}_safetensors")
+    print(f"Saving the converted model to {out_folder} with a maximum shard size of {args.max_shard_size}...")
+    model.save_pretrained(out_folder, max_shard_size=args.max_shard_size, safe_serialization=True)
+    tokenizer.save_pretrained(out_folder)